Perceptual Audio Coding Using N-Channel Lattice Vector Quantization

We consider the problem of reliable distribution of audio over packet-switched networks. We make use of multiple-description coding combined with transform coding in order to obtain robustness towards packet losses. Previous approaches to this problem were restricted to the case of only two descriptions. In this work we use n-channel multiple-description lattice vector quantizers (MD-LVQs), which allow for the possibility of using more than two descriptions. For a given packet-loss probability we find the number of descriptions and the bit allocation between transform coefficients which minimizes a perceptual distortion measure subject to an entropy constraint. The optimal quantizers are presented in closed form, thus avoiding any iterative quantizer design procedures. The theoretical results are verified with numerical computer simulations using audio signals and it is shown that in environments with excessive packet losses it is advantageous to use more than two descriptions. We verify in subjective listening tests that using more than two descriptions lead to signals of perceptually higher quality